{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
   "metadata": {},
   "source": [
    "# Welcome to your first assignment!\n",
    "\n",
    "Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n",
    "            <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n",
    "            <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
    "            Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
   "metadata": {},
   "source": [
    "# HOMEWORK EXERCISE ASSIGNMENT\n",
    "\n",
    "Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n",
    "\n",
    "You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n",
    "\n",
    "**Benefits:**\n",
    "1. No API charges - open-source\n",
    "2. Data doesn't leave your box\n",
    "\n",
    "**Disadvantages:**\n",
    "1. Significantly less power than Frontier Model\n",
    "\n",
    "## Recap on installation of Ollama\n",
    "\n",
    "Simply visit [ollama.com](https://ollama.com) and install!\n",
    "\n",
    "Once complete, the ollama server should already be running locally.  \n",
    "If you visit:  \n",
    "[http://localhost:11434/](http://localhost:11434/)\n",
    "\n",
    "You should see the message `Ollama is running`.  \n",
    "\n",
    "If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve`  \n",
    "And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2`  \n",
    "Then try [http://localhost:11434/](http://localhost:11434/) again.\n",
    "\n",
    "If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29ddd15d-a3c5-4f4e-a678-873f56162724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "\n",
    "OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
    "HEADERS = {\"Content-Type\": \"application/json\"}\n",
    "MODEL = \"llama3.2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dac0a679-599c-441f-9bf2-ddc73d35b940",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a messages list using the same format that we used for OpenAI\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7bb9c624-14f0-4945-a719-8ddb64f66f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload = {\n",
    "        \"model\": MODEL,\n",
    "        \"messages\": messages,\n",
    "        \"stream\": False\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "479ff514-e8bd-4985-a572-2ea28bb4fa40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's just make sure the model is loaded\n",
    "\n",
    "!ollama pull llama3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42b9f644-522d-4e05-a691-56e7658c0ea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generative AI has numerous business applications across various industries. Here are some examples:\n",
      "\n",
      "1. **Content Generation**: Generative AI can be used to generate high-quality content such as articles, social media posts, product descriptions, and more. This can help businesses save time and resources while maintaining consistency in their content.\n",
      "2. **Product Design and Development**: Generative AI can be used to design and develop new products, such as furniture, electronics, and other consumer goods. It can also be used to optimize existing product designs for better performance and efficiency.\n",
      "3. **Marketing Automation**: Generative AI can be used to automate marketing tasks such as email campaigns, ad copywriting, and social media posting. This can help businesses personalize their marketing efforts and reach a wider audience.\n",
      "4. **Customer Service**: Generative AI can be used to power chatbots and virtual assistants that provide customer support and answer frequently asked questions. This can help businesses improve their customer service experience and reduce response times.\n",
      "5. **Data Analysis and Visualization**: Generative AI can be used to analyze large datasets and generate insights, such as identifying trends and patterns. It can also be used to visualize complex data in a more intuitive and user-friendly way.\n",
      "6. **Predictive Maintenance**: Generative AI can be used to predict equipment failures and schedule maintenance tasks. This can help businesses reduce downtime and improve overall efficiency.\n",
      "7. **Supply Chain Optimization**: Generative AI can be used to optimize supply chain operations, such as predicting demand, managing inventory, and identifying bottlenecks.\n",
      "8. **Financial Modeling**: Generative AI can be used to build financial models and predict future market trends. This can help businesses make more informed investment decisions and avoid potential pitfalls.\n",
      "9. **Creative Writing and Art**: Generative AI can be used to generate creative content such as poetry, short stories, and art. This can help businesses tap into new sources of inspiration and innovation.\n",
      "10. **Cybersecurity**: Generative AI can be used to detect and respond to cyber threats in real-time. It can also be used to predict potential vulnerabilities and develop more effective security strategies.\n",
      "\n",
      "Some specific examples of companies using generative AI include:\n",
      "\n",
      "* Google's AutoML (Automated Machine Learning) tool, which uses generative AI to automate machine learning tasks.\n",
      "* Amazon's SageMaker, which provides a range of tools and services for building and deploying generative AI models.\n",
      "* Microsoft's Azure Machine Learning, which offers a range of features and tools for building and deploying generative AI models.\n",
      "* IBM's Watson, which uses generative AI to provide a range of services including customer service, content generation, and predictive maintenance.\n",
      "\n",
      "These are just a few examples of the many business applications of generative AI. As the technology continues to evolve, we can expect to see even more innovative use cases emerge.\n"
     ]
    }
   ],
   "source": [
    "# If this doesn't work for any reason, try the 2 versions in the following cells\n",
    "# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
    "# And if none of that works - contact me!\n",
    "\n",
    "response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
    "print(response.json()['message']['content'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe",
   "metadata": {},
   "source": [
    "# Introducing the ollama package\n",
    "\n",
    "And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n",
    "\n",
    "Under the hood, it's making the same call as above to the ollama server running at localhost:11434"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generative AI has numerous business applications across various industries. Here are some examples:\n",
      "\n",
      "1. **Content Creation**: Generative AI can be used to generate high-quality content such as articles, social media posts, and product descriptions. This can help businesses reduce content creation costs and improve efficiency.\n",
      "2. **Marketing Automation**: Generative AI-powered chatbots can be used to automate customer service, provide personalized recommendations, and enhance the overall customer experience.\n",
      "3. **Product Design**: Generative AI can be used to generate design concepts for products, packaging, and branding. This can help businesses reduce design costs and improve product appeal.\n",
      "4. **Recommendation Systems**: Generative AI can be used to build recommendation systems that suggest products or services based on user behavior, preferences, and search history.\n",
      "5. **Financial Analysis**: Generative AI can be used to analyze financial data, predict market trends, and identify potential investment opportunities.\n",
      "6. **Supply Chain Optimization**: Generative AI can be used to optimize supply chain operations by predicting demand, identifying bottlenecks, and suggesting alternative routes.\n",
      "7. **Customer Service**: Generative AI-powered chatbots can be used to provide 24/7 customer support, answer frequently asked questions, and route complex issues to human agents.\n",
      "8. **Sales Forecasting**: Generative AI can be used to predict sales performance based on historical data, market trends, and competitor activity.\n",
      "9. **Brand Identity**: Generative AI can be used to generate brand identities, logos, and visual styles that are consistent with a company's values and mission.\n",
      "10. **Quality Control**: Generative AI can be used to detect defects in products, analyze quality control metrics, and suggest improvements.\n",
      "\n",
      "Some specific examples of businesses using generative AI include:\n",
      "\n",
      "* Amazon using generative AI to optimize its recommendation system\n",
      "* IBM using generative AI to generate new designs for products and packaging\n",
      "* NVIDIA using generative AI to develop more realistic graphics and animations for gaming and movie production\n",
      "* Siemens using generative AI to optimize supply chain operations and reduce costs\n",
      "\n",
      "Overall, generative AI has the potential to transform businesses by automating tasks, improving efficiency, and providing new insights into customer behavior and market trends.\n"
     ]
    }
   ],
   "source": [
    "import ollama\n",
    "\n",
    "response = ollama.chat(model=MODEL, messages=messages)\n",
    "print(response['message']['content'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4704e10-f5fb-4c15-a935-f046c06fb13d",
   "metadata": {},
   "source": [
    "## Alternative approach - using OpenAI python library to connect to Ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23057e00-b6fc-4678-93a9-6b31cb704bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# There's actually an alternative approach that some people might prefer\n",
    "# You can use the OpenAI client python library to call Ollama:\n",
    "\n",
    "from openai import OpenAI\n",
    "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
    "\n",
    "response = ollama_via_openai.chat.completions.create(\n",
    "    model=MODEL,\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9e22da-b891-41f6-9ac9-bd0c0a5f4f44",
   "metadata": {},
   "source": [
    "## Are you confused about why that works?\n",
    "\n",
    "It seems strange, right? We just used OpenAI code to call Ollama?? What's going on?!\n",
    "\n",
    "Here's the scoop:\n",
    "\n",
    "The python class `OpenAI` is simply code written by OpenAI engineers that makes calls over the internet to an endpoint.  \n",
    "\n",
    "When you call `openai.chat.completions.create()`, this python code just makes a web request to the following url: \"https://api.openai.com/v1/chat/completions\"\n",
    "\n",
    "Code like this is known as a \"client library\" - it's just wrapper code that runs on your machine to make web requests. The actual power of GPT is running on OpenAI's cloud behind this API, not on your computer!\n",
    "\n",
    "OpenAI was so popular, that lots of other AI providers provided identical web endpoints, so you could use the same approach.\n",
    "\n",
    "So Ollama has an endpoint running on your local box at http://localhost:11434/v1/chat/completions  \n",
    "And in week 2 we'll discover that lots of other providers do this too, including Gemini and DeepSeek.\n",
    "\n",
    "And then the team at OpenAI had a great idea: they can extend their client library so you can specify a different 'base url', and use their library to call any compatible API.\n",
    "\n",
    "That's it!\n",
    "\n",
    "So when you say: `ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')`  \n",
    "Then this will make the same endpoint calls, but to Ollama instead of OpenAI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90",
   "metadata": {},
   "source": [
    "## Also trying the amazing reasoning model DeepSeek\n",
    "\n",
    "Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B.  \n",
    "This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n",
    "\n",
    "Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!ollama pull deepseek-r1:1.5b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d3d554b-e00d-4c08-9300-45e073950a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n",
    "\n",
    "response = ollama_via_openai.chat.completions.create(\n",
    "    model=\"deepseek-r1:1.5b\",\n",
    "    messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
   "metadata": {},
   "source": [
    "# NOW the exercise for you\n",
    "\n",
    "Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A class to represent a Webpage\n",
    "# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
    "\n",
    "# Some websites need you to use proper headers when fetching them:\n",
    "headers = {\n",
    " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
    "}\n",
    "\n",
    "class Website:\n",
    "\n",
    "    def __init__(self, url):\n",
    "        \"\"\"\n",
    "        Create this Website object from the given url using the BeautifulSoup library\n",
    "        \"\"\"\n",
    "        self.url = url\n",
    "        response = requests.get(url, headers=headers)\n",
    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d29564f8-13f6-48b6-ab0b-450e53f3e3aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "ed = Website(\"https://edwarddonner.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "721a4ec9-0b66-419d-92e1-8b24e9a38b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function that writes a User Prompt that asks for summaries of websites:\n",
    "\n",
    "def user_prompt_for(website):\n",
    "    user_prompt = f\"You are looking at a website titled {website.title}\"\n",
    "    user_prompt += \"\\nThe contents of this website is as follows; \\\n",
    "please provide a short summary of this website in markdown. \\\n",
    "If it includes news or announcements, then summarize these too.\\n\\n\"\n",
    "    user_prompt += website.text\n",
    "    return user_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0ae9815-4643-4fc7-88d0-72db83fa569f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are looking at a website titled Home - Edward Donner\n",
      "The contents of this website is as follows; please provide a short summary of this website in markdown. If it includes news or announcements, then summarize these too.\n",
      "\n",
      "Home\n",
      "Connect Four\n",
      "Outsmart\n",
      "An arena that pits LLMs against each other in a battle of diplomacy and deviousness\n",
      "About\n",
      "Posts\n",
      "Well, hi there.\n",
      "I’m Ed. I like writing code and experimenting with LLMs, and hopefully you’re here because you do too. I also enjoy DJing (but I’m badly out of practice), amateur electronic music production (\n",
      "very\n",
      "amateur) and losing myself in\n",
      "Hacker News\n",
      ", nodding my head sagely to things I only half understand.\n",
      "I’m the co-founder and CTO of\n",
      "Nebula.io\n",
      ". We’re applying AI to a field where it can make a massive, positive impact: helping people discover their potential and pursue their reason for being. Recruiters use our product today to source, understand, engage and manage talent. I’m previously the founder and CEO of AI startup untapt,\n",
      "acquired in 2021\n",
      ".\n",
      "We work with groundbreaking, proprietary LLMs verticalized for talent, we’ve\n",
      "patented\n",
      "our matching model, and our award-winning platform has happy customers and tons of press coverage.\n",
      "Connect\n",
      "with me for more!\n",
      "May 28, 2025\n",
      "Connecting my courses – become an LLM expert and leader\n",
      "May 18, 2025\n",
      "2025 AI Executive Briefing\n",
      "April 21, 2025\n",
      "The Complete Agentic AI Engineering Course\n",
      "January 23, 2025\n",
      "LLM Workshop – Hands-on with Agents – resources\n",
      "Navigation\n",
      "Home\n",
      "Connect Four\n",
      "Outsmart\n",
      "An arena that pits LLMs against each other in a battle of diplomacy and deviousness\n",
      "About\n",
      "Posts\n",
      "Get in touch\n",
      "ed [at] edwarddonner [dot] com\n",
      "www.edwarddonner.com\n",
      "Follow me\n",
      "LinkedIn\n",
      "Twitter\n",
      "Facebook\n",
      "Subscribe to newsletter\n",
      "Type your email…\n",
      "Subscribe\n"
     ]
    }
   ],
   "source": [
    "print(user_prompt_for(ed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf718345-9084-4a16-ae1c-6099b4c82d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "def messages_for(website):\n",
    "    return [\n",
    "        {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac40aa1a-3121-471d-bd9a-12eab4daa063",
   "metadata": {},
   "outputs": [],
   "source": [
    "payloadExercise = {\n",
    "        \"model\": MODEL,\n",
    "        \"messages\": messages_for(ed),\n",
    "        \"stream\": False\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d79ad65-37de-413e-bd2e-4e99aad46d5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Summary of the Website**\n",
      "==========================\n",
      "\n",
      "### About the Owner\n",
      "\n",
      "The website is owned by Edward Donner, a co-founder and CTO of Nebula.io, an AI startup that applies machine learning (LLM) to help people discover their potential. He has previous experience as the founder and CEO of another AI startup, untapt, which was acquired in 2021.\n",
      "\n",
      "### Latest News/Announcements\n",
      "\n",
      "*   **Courses:** Edward Donner is offering courses on LLM expert and leader development.\n",
      "    *   January 23, 2025: The Complete Agentic AI Engineering Course\n",
      "    *   May 28, 2025: Connecting my courses – become an LLM expert and leader\n",
      "    *   Other upcoming courses include \"LLM Workshop – Hands-on with Agents – resources\"\n",
      "*   **AI Executive Briefing:** A series of events for executive-level individuals.\n",
      "    *   April 21, 2025: 2025 AI Executive Briefing\n"
     ]
    }
   ],
   "source": [
    "# If this doesn't work for any reason, try the 2 versions in the following cells\n",
    "# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
    "# And if none of that works - contact me!\n",
    "\n",
    "responseExercise = requests.post(OLLAMA_API, json=payloadExercise, headers=HEADERS)\n",
    "print(responseExercise.json()['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b8420ce-1934-4dbf-8f46-d5accbce9560",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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